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AI Agent Examples and Use Cases

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Wrike Documentation Team

Wrike Documentation Team

TL;DR

Learn how to use AI agents in Wrike to automate repeatable work like drafting updates, routing requests, managing approvals, and detecting risks. Explore example agents by type, common workflows, and copy-ready setups (prompts, triggers, actions) from the AI Agents Library.

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Overview

AI agents can help you handle repeatable work, speed up handoffs, and keep projects moving. This article shares example agents by type, common workflows, and practical setups you can use as a starting point.
Read our public AI Agents Library on the Wrike Community for full agent configurations (prompts, triggers, actions) that anyone can copy and deploy.

Review AI Agent Examples by Type

You can use AI agents for different kinds of work across your account.

Content and Communication AI Agents

These agents help you draft, summarize, or rewrite content.

Examples:

  • Draft project updates from task activity
  • Summarize long comment threads
  • Rewrite text for a different audience or tone
  • Create meeting follow-ups from notes

Project Coordination AI Agents

These agents help you manage work and keep teams aligned.

Examples:

  • Create tasks from request details
  • Route work to the right assignee or team
  • Flag overdue work and notify owners
  • Update statuses based on task changes

Review and Approval AI Agents

These agents support review cycles and decision-making.

Examples:

  • Check requests for missing information
  • Send reminders when approvals are due
  • Summarize feedback before the next review
  • Escalate stalled approvals

Reporting and Insight AI Agents

These agents help you spot trends and share updates.

Examples:

  • Build weekly status summaries
  • Surface project risks
  • Highlight blocked tasks
  • Share workload or progress snapshots

Use Common Workflow Examples

Here are a few common ways teams use AI Agents in Wrike.

Intake and Triage

Use an agent to review incoming requests, check for required details, and route work to the right team.

Typical setup:

  • Trigger: Request submitted
  • Action: Review request content
  • Action: Tag, assign, or create follow-up work

Status Updates

Use an agent to collect task activity and draft a project update.

Typical setup:

  • Trigger: Scheduled time or status change
  • Action: Review recent task activity
  • Action: Draft and post a summary

Approval Follow-up

Use an agent to monitor approvals and notify stakeholders when work is waiting.

Typical setup:

  • Trigger: Approval not completed by due date
  • Action: Check approval status
  • Action: Send reminder or escalate

Risk Detection

Use an agent to look for warning signs, such as overdue tasks, blocked work, or missing owners.

Typical setup:

  • Trigger: Scheduled scan
  • Action: Review project data
  • Action: Post a risk summary or notify the project owner

Build a Practical Setup

When you copy an example from the library, look for these parts:

  • Prompt: What the agent should do
  • Trigger: What starts the agent
  • Actions: What the agent does next
  • Conditions: Rules that control when it runs

Start with a simple setup:

  1. Pick one repeatable workflow.
  2. Choose an example agent that matches that workflow.
  3. Copy the prompt, trigger, and actions.
  4. Update the setup for your team.
  5. Test the agent with sample work before you use it at scale.

Important: Do not publish an agent without testing it first. Small prompt or trigger changes can affect results.

Tips for Choosing the Right Example

  • Start with one clear use case
  • Use simple prompts first
  • Review actions before you turn the AI agent on
  • Test with low-risk tasks or projects
  • Update the setup based on real results

What’s Next?

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